Prevalence of Sleep-Disordered Breathing in Prader–Willi Syndrome

Introduction Sleep-disordered breathing (SDB) is common in patients with Prader–Willi Syndrome (PWS). However, the prevalence of SDB varies widely between studies. Early identification of SDB and factors contributing to its incidence is essential, particularly when considering growth hormone (GH) therapy. Objectives The aims of the study were to describe the prevalence and phenotypes of sleep-disordered breathing (SDB) in patients with Prader–Willi syndrome (PWS) and to determine the effects of age, gender, symptoms, GH therapy and body mass index on SDB severity. Methods This study was a retrospective chart review of all patients with genetically confirmed Prader–Willi syndrome who underwent diagnostic overnight polysomnography (PSG) in the sleep laboratory at Sidra Medicine. Clinical and PSG data of enrolled patients were collected. Results We identified 20 patients (nine males, eleven females) with PWS who had overnight sleep polysomnography (PSG) at a median age (IQR) of 5.83 (2.7–12) years. The median apnea-hypopnea index (AHI) was 8.55 (IQR 5.8–16.9) events/hour. The median REM-AHI was 27.8 (IQR 15–50.6) events/hour. The median obstructive apnea-hypopnea index (OAHI) was 7.29 (IQR 1.8–13.5) events/hour. The median central apnea-hypopnea index (CAHI) was 1.77 (IQR 0.6–4.1) events/hour. Nineteen patients (95%) demonstrated SDB by polysomnography (PSG) based on AHI ≥1.5 events/hour. Nine patients (45%) were diagnosed with obstructive sleep apnea (OSA). Three patients (15%) were diagnosed with central sleep apnea (CSA). Seven patients (35%) were diagnosed with mixed sleep apnea. No correlations were observed between AHI and age, gender, BMI, symptoms, or GH therapy. However, REM-AHI was significantly correlated with BMI (P=0.031). Conclusion This study shows a high prevalence of SDB among our patients with PWS. Obstructive sleep apnea was the predominant phenotype. BMI was the only predictor for high REM-AHI. Further studies of large cohorts are warranted to define SDB in PWS and design the appropriate treatment.


Introduction
Prader-Willi syndrome (PWS) is a rare genetic disorder characterized by the absence of the expression of the paternally inherited genes in chromosome 15 q11-13 region [1].Te estimated prevalence of PWS is one in 10,000-25,000 live births [2].Patients with PWS can have multisystem abnormalities that include neurodevelopmental delay, growth retardation, endocrine and metabolic disturbances, and behavioral disorders that vary with age.During infancy, the main clinical features of PWS include hypotonia, feeding difculties, and poor growth.Patients develop hyperphagia from childhood and onward due to hypothalamic dysfunction and consequent morbid obesity.Other manifestations include hypogonadism, psychomotor delay, hypothyroidism, and short stature [1,2].
Sleep-related breathing disorders (SDBs) are common and potentially serious complications of PWS that can afect patients at any age.Multiple studies have reported a high prevalence of SDB among individuals with PWS ranging between 44 and 100%, compared to a prevalence of 2-3% in the general population [1][2][3].Craniofacial dysmorphology afecting upper airway size, adenotonsillar hypertrophy, obesity, hypotonia, chest wall deformities, and defective ventilatory control due to hypothalamic dysfunction contribute to the overall high prevalence of SDB in PWS [1,2].
Te spectrum of sleep-related disorders seen in PWS includes central sleep apnea (CSA), alveolar hypoventilation, and obstructive sleep apnea (OSA), with OSA being the most dominant form [1,4].Other patterns of SDB include altered sleep architecture, excessive daytime sleepiness, and abnormal ventilatory response to hypercapnia and hypoxia [4].Te trajectory of SDB in PWS evolves with age from predominantly CSA in infants to OSA in older children [5].Since sleep-related disorders in PWS is a major contributor to the increase in morbidity and mortality in children with PWS, surveillance polysomnography (PSG) has been advocated in children with PWS to detect early SDB and promptly provide the necessary treatment [1,6].
To date, the prevalence, severity, and treatments of SDB among patients with PWS in Qatar have not been reported.Our study aims to describe the prevalence, severity, and treatments prescribed for SDB in patients with PWS who underwent PSG study in the only dedicated pediatric sleep lab in the country.We also aim to evaluate cohort specifc clinical characteristics that can potentially contribute to SDB in our population.

Methods
Tis is a retrospective chart review of all patients with genetically confrmed PWS who underwent full-night polysomnography (PSG) between September 1 st , 2019, and July 30 th , 2022, at Sidra Medicine, which is a tertiary care hospital in the state of Qatar and houses the only pediatric sleep lab in the country.
Te electronic medical records were utilized to obtain patients' demographic and anthropometric characteristics including age, sex, height, and weight at the time of PSG.Body mass index (BMI) was calculated as weight (kg)/height (m 2 ).BMI z-score was calculated using the most recent age and sex specifc growth charts published by the World Health Organization (WHO) [7].Obesity was defned as BMI>90th percentiles or as BMI z-score>2.Reported symptoms, history of adenotonsillectomy, and GH treatment were also collected.
2.1.Statistical Analysis.Demographic, anthropologic, and clinical characteristics were summarized as the mean and standard deviation (SD) for symmetrically distributed continuous variables and median and range for the skewed continuous variables.Scatterplots were drawn between age and BMI with AHI, and Spearman's rank correlation was used to determine the correlation between the skewed variables.Positively skewed variables (AHI and REM-AHI) were log-transformed.Te relationships between logged outcome variables (AHI and REM-AHI) and age, gender, BMI, symptomatic, and GH therapy were assessed using linear regression analysis.Predictor variables' selection in regression analysis was carried out based on the clinical importance.All statistical analyses were performed using STATA IC/16.0 (StataCorp LLC, College Station, Texas, USA).
Multiple regression analysis did not show any signifcant correlation between AHI, and all examined clinical variables (i.e., age, gender, BMI, symptoms, and GH therapy).On the other hand, REM-AHI was signifcantly correlated with BMI (P � 0.031) (Table 3).
Only seven patients (35%) were previously treated with growth hormone.Six patients with OSA were treated with positive airway pressure support (CPAP or BiPAP).One patient had moderate OSA, and the other fve had severe OSA.One patient with moderate OSA was treated with night-time O 2 by nasal cannula.However, six patients with moderate OSA and three with severe OSA received no treatment by the time of the study.All the three patients who were diagnosed with hypoventilation were treated with BiPAP.

Discussion
In our patient population of PWS, we found very high prevalence of sleep-disordered breathing reaching 95%.Te predominant form of SDB observed was OSA.To lesser extent, central sleep apnea and mixed apneas were noted.Te true prevalence of SDB in children with PWS has been challenging to determine because of methodological variations, small sample sizes, and age diferences among studies.Despite this wide variation, the reported prevalence of SDB in PWS is high, ranging between 44% and 100% [1,2].In one meta-analysis of 14 studies, Sedky et al. estimated average prevalence of OSA in children with PWS to be 79.91%[9].
Te most commonly reported phenotypes of SDB in PWS are OSA, CSA, and hypoventilation [5].However, the type of SDB varies with age.CSA is more frequently found among infants than older children with PWS [1,4].In one study, the reported prevalence of CSA among infants<1year of age was high (53%) compared to OSA (11%) [10].In another study, prevalence of CSA was reported to be 71.8%among children<2 years compared to 25% in child-ren>2 years of age [3].Te underlying mechanism of this high CSA in infants with PWS is not entirely understood.It has been proposed that hypotonia, immaturity of the brain stem, and hypothalamic dysfunction in infants could be the underlying etiology [2].On the other hand, OSA is much more common in older children with PWS.Cohen et al. reported signifcant predominance of OSA (52%) compared to CSA (5%) among children with PWS who are>2 years old [11].In our patient cohort, the dominant SDB phenotype was OSA (45%), while CSA was observed in only 15%.Te higher prevalence of OSA in our population is probably related to the high-median age of the included patients (5.8 years).
Te discrepancy seen in the prevalence of reported phenotypes of SDB among diferent cohorts could be related to diferences in criteria used to defne the phenotypes of SDB, in particular central sleep apnea (CSA) and mixed sleep apnea.Previously published PSG studies in PWS use CAHI≥5 events/hour as the criteria for diagnosis of CSA arguing that CAHI of up to 5 event per hour is expected in normal children more than one year of age [10,12].Using this defnition of CAHI>5 events/hour, only one patient (5%) had CSA and three patients (15%) had mixed apnea in  our cohort.However, some of our patients had abnormal SDB, defned as AHI≥1.5 events/hour, with CAHI≥1.5 but <5 events/hour.We argue that these patients cannot be considered completely normal as suggested by previous studies but probably have mild abnormal control of breathing.Future longitudinal studies are needed to determine if these patients develop disease progression and have higher long-term morbidity.Among our patients with OSA, three patients (15%) had mild disease, eight patients (40%) had moderate disease, and eight patients (40%) had severe disease.Others reported that among individuals with OSA, 53.07%had mild OSA, 22.35% moderate OSA, and 24.58% severe OSA [9].Unlike CSA, OSA is a well-known and potentially serious sleep-disordered breathing in PWS.Canora et al. reported OSA in 92.9% of patients with PWS.Te mean obstructive apnea-hypopnea index (OAHI) in their cohort was 7.6 ± 4.2 events/h [12].Our patient cohort had median OAHI of 7.29 events/hour.
Tere are limited studies investigating sleep architecture in individuals with PWS.Our patients demonstrated a reasonably normal sleep architecture with a mean sleep efciency of 83.1%.Te median REM% was normal at 20.85% (14.9-41.5)minutes, and the median REM latency was 64.5 (4.5-254.5)minutes.Similarly, Lin et al. reported the REM latency of 67.4 ± 30.0 minutes and REM percentage of 21.1 ± 5.7% [13].Nevertheless, larger sample size studies of similar age cohorts are required to precisely defne the characteristics of the sleep architecture among patients with PWS.
Obesity in general has been recognized as a predictor of OSA by several studies [15,16].OSA risk increases by 12% for every 1 kg/m 2 increase in BMI above the 50th percentile among healthy children [15].In addition to the impact of obesity on respiratory mechanics, it also worsens the abnormal ventilatory responses to hypoxia and hypercapnia in PWS patients [16].As expected, patients in our cohort were overweight.Te BMI z-score was 3.77 (IQR 2.7-4.6),consistent with the reported high prevalence of obesity in patients with PWS [3].However, the link between BMI and SDB in PWS is controversial.Some studies showed increased SDB with increasing BMI, while others failed to demonstrate this correlation [1,12,17].Sedky et al.'s meta-analysis revealed that OSA in PWS patients increased with greater BMI (r � 0.34, P � 0.018) [9].In contrast, our study did not show a correlation between BMI and AHI, but there was signifcant correlation between BMI and REM-AHI (P � 0.031).Further studies are required to determine if sleep-related breathing abnormalities in PWS are related to obesity alone or to other factors such as hypotonia, control of breathing, or facial dysmorphism.
Several treatment modalities for SDB in PWS have been proposed based on SDB pattern and severity.Infants with CSA are generally treated with nocturnal supplemental oxygen [2,11].In children with moderate to severe OSA, adenotonsillectomy is recommended.Several studies have demonstrated partial response or no response to adenotonsillectomy in PWS [1,9,18].Strategies for weight loss can lead to improvement of OSA in obese PWS patients [1,4].In patients with persistent moderate to severe OSA, continuous positive airway pressure ventilation is a suitable alternative [4].Treatment with GH improves height growth, body composition, lean muscle mass, and subsequently improving muscle strength.Terefore, most studies favor GH treatment for SDB in PWS [1,4,19].However, emerging evidence raises concern of worsening OSA after GH therapy.Treatment with GH is associated with high insulin-like growth factor 1 (IGF-1) levels which may lead to enhanced growth of upper airway lymphoid tissues, including adenotonsillar hypertrophy which may contribute to OSA within frst two years of therapy [1,4].Because of the reported potential worsening of OSA after GH therapy [20,21], consensus guidelines recommend a screening PSG before initiation and within 3-6 months after the start of GH therapy in all children with PWS [22].Our study included only few patients who were already on GH therapy.No correlation between GH treatment and SDB has been demonstrated.However, the limited sample size may infuence this observation.

Canadian Respiratory Journal
Sleep studies were performed in our patients based on PWS diagnosis regardless of symptoms.History of snoring and/or sleep apnea did not correlate with AHI or REM-AHI which supports the recommendation to screen patients with PWS for SDB even in case of absence of symptoms.

Conclusion
Our study shows a high prevalence of SDB in PWS independent of symptoms supporting the recommendation for screening all individuals with PWS patients using polysomnography.Obstructive sleep apnea was the predominant phenotype of SDB.Central apnea is less frequent which could be due to patients' age.However, CSA in PWS is probably underestimated in previously published studies due to use of high threshold in the diagnostic criteria for CSA, which needs further investigation.

Table 2 :
PSG data of the patients with PWS (n � 20).
Data are expressed as mean ± standard deviation or median (IQR).Categorical variables are expressed as frequencies and percentages.WASO, wakefulness after sleep onset; AHI, apnea-hypopnea index; OAHI, obstructive apnea-hypopnea index; CAHI, central apnea-hypopnea index; CSA, central sleep apnea; OSA, obstructive sleep apnea; TST, total sleep time; ETCO 2 , end-tidal CO 2 ; SpO 2 , O 2 saturation.Te table provides descriptive data for the entire population.Te bold values represent the median and interquartile range.

Table 1 :
Clinical characteristics of the patients with PWS (n � 20)., body mass index; GH, hormone therapy.Numerical variables are expressed as * median (IQR) and categorical variables as frequencies and percentages.Te table provides descriptive data for the entire population.Te bold values represent the median and interquartile range. BMI

Table 3 :
Regression analysis for AHI and REM-AHI (univariate analysis) with age, BMI, symptoms, GH therapy and AHI, REM-AHI in patients with PWS. , body mass index; GH, hormone therapy.Estimates of efect size are presented as odds ratios and 95% confdence intervals as exponentiated β-coefcients (e β ) for the continuous logged variables for AHI and REM-AHI.Exponentiated β-values indicate the percentage change in the outcome per unit change in the predictor variable. BMI